Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran

Dadashpoor, Hashem; Salarian, Fardis

doi:10.1007/s10668-018-0211-2

Cited by 52 publications

(29 citation statements)

References 38 publications

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“…According to the coefficients of ROC in (Table 4), the accuracy of agricultural lands modeling is less than the other three uses, which is justified by the fact that this use is changing over the year; and also another reason may relate to the low accuracy of the classification of this use. Built-up use has higher accuracy than other uses, which can be because the new areas are created by certain rules, but in other uses, there are no specific rules for creating changes (Dadashpoor & Salarian 2020). Accordingly, the land-use map in 2015 was modeled.…”

Section: Validation Of Lr and Camentioning

confidence: 99%

Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region

Dadashpoor

Panahi

2021

Environ Dev Sustain

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Spatial simulation of land-use change scenarios in metropolitan areas is essential for analyzing both the causes and consequences of various future scenarios and is also valuable for land-use planning and management. However, current simulation models primarily focus on spatial and rarely on quantitative driving factors. This article aims to simulate future scenarios of land-use changes in the Tehran metropolitan region (TMR) by combining different models to fill this gap. Thus, in the first step, land-use changes were analyzed in the period 1985, 2000, and 2015. Then, by identifying the impact of driving factors and land-use transition potentials with Logistic regression (LR), land-use changes were allocated using the Cellular Automata (CA) method. Finally, with the validation of the model, four scenarios of the current trend(CT), socioeconomic growth(SEG), ecologicaloriented(EO), and integrated development(ID) were suggested with the combination of the System Dynamic (SD) model. The results show that the trend of land-use changes in TMR has led to the destruction of grassland, agricultural, and uncultivated lands and the continuation of this trend will increase the damage of built-up areas on valuable natural and ecological resources. In this way, proximity to roads, distance from built-up areas, and natural factors had the greatest impact on changes. Based on future scenarios in 2030, the change in the SEG-scenario shows a rapid increase in built-up areas (2858km 2) and encroachment on agricultural lands (2171km 2) . In the EO-scenario, destruction of grassland and agricultural lands and the growth of built-up areas will be limited, while CT-scenario leads to the high growth of built-up areas along with destructive impacts on natural and open spaces. In the ID-scenario, the built-up areas and grasslands will increase to 2808km 2 and 7438km 2 , respectively. Accordingly, policy-makers can use simulation of different scenarios to mitigate probable consequences of land-use changes in the metropolitan regions.

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Section: Validation Of Lr and Camentioning

confidence: 99%

Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region

Dadashpoor

Panahi

2021

Environ Dev Sustain

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“…These efforts should be conducted at every specific block size to avoid excessive aggregation of built-up lands. After all, the aggregation of impervious surface and population could bring many environmental problems affecting human heath, such as urban heat island effect, loss of biodiversity and enhanced CO 2 emission (Ali et al, 2019;Bihamta et al, 2014;Dadashpoor & Salarian, 2018;Min et al, 2019).…”

Section: How Does Gwr Model Performedwith Respect To Block Scale Andmentioning

confidence: 99%

Effects of Changing Scales on Landscape Patterns and Spatial Modeling Under Urbanization

Yang

Wang

et al. 2020

Journal of Environmental Engineering and Landscape Management

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Spatial scale is an eternal topic in landscape pattern related analysis. This paper examined the spatial scale effect of landscape pattern changes and their relationships with urbanization indicators in Qingdao using a series of sampling blocks. The results indicated that, with the increasing block scale, the mean patch density and aggregation within a block decreased, whereas the diversity increased. Furthermore, the expanding scale amplified the mean change ratio of landscape metrics and eliminated local drastic changes and regional variation trends along an urban-to-rural gradient, which would be obvious at a finer block scale. Meanwhile, the adjusted R2 of GWR (Geographically Weighted Regression) models increased with an increasing block size, especially when the block scale changed from 1 km to 5 km. Odd-numbered block scales performed better than even-numbered block scales.

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“…A LANDSCAPE is a heterogeneous land area containing multiple ecosystems or a mosaic of different land use/cover [1]. The types, proportions, and spatial arrangements of the landscape ecosystems or land use/cover [2]- [4]-often known as the landscape pattern-are considerably influenced by nature and humans, which may influence ecologic processes and the resulting biodiversity, with profound effects on the ecological, social, and economic functions of systems [5], [6].…”

Section: Introductionmentioning

confidence: 99%

State-and-Evolution Detection Models: A Framework for Continuously Monitoring Landscape Pattern Change

Tian

Liu

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Detecting the evolution of large-area landscape patterns using long-term remote-sensing images is helpful in supporting research on the relationship between landscape patterns and ecological processes, as well as the development of ecological process simulations and spatiotemporal interaction models. However, detection methods have generally been developed as separate applications, each with a separate type of landscape pattern change; remote-sensing images are acquired at epochal timesteps. Consequently, in practical applications, many omission changes for some types of pattern changes and inaccurate evolution time are presented in the detected map. In this article, stateand-evolution detection models (SEDMs) are promoted to obtain complete information about the evolution of landscape patterns based on yearly land cover data. In the proposed framework, we first define the major categories of landscape pattern changes to comprehensively reveal the characteristics of landscape pattern changes associated with real change cases. Next, a morphological rule-based pattern recognition approach is proposed for quantitative discrimination among these categories. This approach is then applied in annual land cover data to continuously detect landscape pattern evolution processes and evolution time. Finally, the detected evolution time in different evolution processes is applied to measure the timestep between two disparate types. The performances of the SEDMs are presented by Landsat-derived land cover evolution in Shanxi, China. The detected results are indirectly verified by the land cover conversion matrix and connect index, indicating strong robustness and generalization ability of the SEDMs.

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Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran

Cited by 52 publications

References 38 publications

Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region

Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region

Effects of Changing Scales on Landscape Patterns and Spatial Modeling Under Urbanization

State-and-Evolution Detection Models: A Framework for Continuously Monitoring Landscape Pattern Change

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